Males had higher ET-1 levels. The patients were divided into two groups based on their pre-HD pulse wave velocity (PWV): PWV ≥ 12 m/s and PWV < 12 m/s. The pre-HD and mid-HD SDC-1 levels were higher in the group with a PWV ≥ 12 m/s . The post-HD ET-1 levels were higher in the patient group with a PWV ≥ 12 m/s . Patients with a PWV ≥ 12 m/s had higher pre-HD peripheral and aortic systolic blood pressures (p < 0.05). The total cholesterol correlated with the SDC-1 decrease during HD . The pre-, mid-, and post-HD SDC-1 correlated with ultrafiltration . Conclusion: SDC-1 and ET-1 contribute to the vascular changes observed during HD, and they have correlations with some cardiovascular risk factors.Background: Chronic hemodialysis (HD) patients have a very high cardiovascular risk. Acute vascular changes during dialysis mediated by factors of the endothelium may have a crucial role in this. The aim of this article is to study the acute vascular changes during HD. Methods: In 29 consecutive chronic HD patients (age: 65.6 ± 10.4 years), their pre-, mid-, and post-HD plasma syndecan-1 (SDC-1) and endothelin-1 (ET-1) levels were measured. Applanation tonometry was performed before HD. Results: Their SDC-1 levels increased during HD ( The incidence and prevalence of chronic kidney disease (CKD) are increasing; the number of patients may be around 160 million worldwide ,3. CardiThe functional integrity of the endothelium can be compromised, having a crucial role in the dysfunction of the vascular endothelium, leading to acute and chronic hemodynamic changes and also to the development of hypertension and other cardiovascular disorders in CKD patients .The salt- and volume-dependent hypertension of chronic HD patients and the hypotension developing as a result of ultrafiltration are much-studied topics, but their pathophysiology is not completely understood. It has been proven that both hypertension and post-dialysis hypotension have a negative effect on the survival of patients . These bThe endothelial glycocalyx (GCX), a carbohydrate-rich mesh of large anionic polymers, lines the luminal side of the endothelium. It is a highly hydrated, negatively charged “firewall” that protects the endothelium from pathogenic insults. These negatively charged macromolecules can bind and reversibly store sodium, providing a first-line barrier against sodium overload in the endothelial cells . Its comPlasma syndecan-1 (SDC-1) is a heparan sulfate proteoglycan expressed in endothelial cells and the main marker of endothelial GCX degradation. An elevated serum level of SDC-1 is associated with endothelial injury and can be used to characterize GCX damage. SDC-1 plays an important role in cell migration, differentiation, and proliferation. It protects the endothelial GCX from harmful effects and contributes to the attachment of platelets, leukocytes, and inflammatory cells on the surface of the endothelium. SDC-1 is a promising biomarker for the diagnosis and prognosis of vascular diseases ,20.The role of endothelin-1 (ET-1) in the progression of chronic kidney disease and the development of hypertension is relatively well known. ET-1 is a peptide hormone produced and released by endothelial cells. It acts on various tissues and cells and is involved in several physiological processes. As it is also a potent vasoconstrictor, it increases one’s blood pressure. It may play a role in CV diseases such as hypertension, heart attacks, and strokes. ET-1 plays an important role in cell migration, cell division, the regulation of cell division, the regulation of cell functions, and the maintenance of cell composition. However, its effects seem to be sensitive to changes in the extracellular environment and the physiological state of the cells ,22.We have relatively little data on the injury of the GCX in hemodialysis patients and on the role of SDC-1 and ET-1 in acute hemodynamic changes during hemodialysis treatment elicited mainly by the ultrafiltration process. Therefore, particular attention was paid to estimating the importance of GCX in HD-related acute hemodynamic changes in patients in regular HD.The purpose of this study was to measure the SDC-1 and ET-1 levels before, during, and after HD to clarify their role in the acute hemodynamic changes and to find a correlation between the SDC-1 and ET-1 levels, central and peripheral BP, and the CV risk factors.This study was designed to assess the time course of vascular responses to HD sessions by measuring SDC-1, ET-1, and PWV, exploring the interrelationships of these indices of vascular functions and revealing their association with some clinical and laboratory parameters.We selected 29 consecutive chronic HD patients from 125 patients, who participated in this prospective, cross-sectional descriptive study, conducted at the Fresenius Medical Care Dialysis Centers of Pécs. One patient withdrew his informed consent. Clinically stable chronic HD patients who had been in the dialysis program for at least 3 months were included. The eligibility criteria included an age of >18 years old, no malignant disease, no acute or chronic infection, and a stable clinical status.Patients with lower-extremity amputations, any acute infection, malignancies, acute myocardial infarction, pulmonary edema, or hemodynamic instability were excluded from this study.The underlying renal pathologies that progressed to ESKD included the following: diabetic nephropathy (24%), nephrosclerosis (26%), chronic glomerulonephritis (15%), polycystic kidney disease (10%), chronic interstitial nephritis (10%), renovascular disease (2%), and other/unknown causes (13%). Most of the patients (85%) received antihypertensive therapy.The patients underwent three HD sessions per week, each lasting for 4 h. Online hemodiafiltration was carried out using Fresenius 5008 B  equipment with Helixone/Fresenius polysulfone high-flux dialyzer membranes. Their body mass index (BMI) was calculated.The patients were divided into two groups based on their pre-HD pulse wave velocity (PWV < 12 m/s vs. PWV ≥ 12 m/s), as a PWV < 12 m/s is considered normal by the ESC/ESH guidelines  Figure .Their BP was measured with a calibrated automated device  integrated blood pressure module) using an appropriate cuff size. Their carotid–femoral pulse wave velocity (cfPWV) and augmentation index (AIx) were measured via applanation tonometry  before the HD treatment. Pulse wave recordings were consecutively performed at two different arterial palpation locations . All recorded values conformed to the quality control standards incorporated in the software package provided by the manufacturer. All measurements were performed by the same operator.Routine biochemical parameters were measured via the standard methods. Serum ET-1 and SDC-1 were measured via human enzyme-linked immunoassay (ELISA) kits  in samples obtained pre-, mid-, and post-HD.We measured all the patients’ fluid overload statuses via a bioimpedance Body Composition Monitor  before the hemodialysis session. Overhydration (OH) and overhydration extracellular water (OH ECW) parameters were used to assess the patients’ volume statuses.p < 0.05. The normality of the data was determined using the Kolmogorov–Smirnov test. Correlations between continuous variables were determined by calculating linear regression using the Pearson test. The data were presented as the mean ± SD in cases of a normal distribution and as the median (lower or upper quartile) in cases of a nonnormal distribution. Trend analysis was performed using Wilcoxon’s methods. A univariate analysis of variance was used to investigate the potential effect of covariates in a model. A backward multiple regression analysis was performed to determine the relative influence of the selected independent variables on the variance of the dependent variable in the constructed model. Statistical analyses were performed using SPSS  version 21.0 software.The results are presented with a frequency and average confidence range. The significance level was determined at This research was approved by the Local and Regional Ethical Committee (9537-PTE2023). The investigation conformed to the principles outlined in the World Medical Association Declaration of Helsinki. Informed written consent was obtained from all the participants.The clinical and baseline laboratory data of the patients are presented in p = 0.004)  A. A decr= 0.004) B, which p = 0.001) higher than that in women . Similarly, the mid-HD and post-HD SDC-1 levels were also significantly higher in men than in women.The pre-HD SDC-1 level in men (10.547 ng/mL) was significantly (The patients were divided into two groups based on their pulse wave velocity (PWV < 12 m/s vs. PWV ≥ 12 m/s). No significant difference was observed in the baseline parameters of the two patient groups, except for their age and hemoglobin level . The grop = 0.013, and 10.319 ± 3.482 vs. 8.248 ± 1.793 ng/mL, p = 0.044, respectively). There was no difference in the post-HD SDC-1 levels between the groups . There was no significant difference in the pre-HD and mid-HD ET-1 levels between the groups . No effect of the above-mentioned covariates could be detected.A significant positive correlation was found between the difference in the pre-HD and post-HD SDC-1 levels (ΔSDC-1) and the serum total cholesterol levels .p = 0.019, p = 0.044, and p = 0.012, respectively, Pre-, mid-, and post-HD SDC-1 had a significant correlation with the ultrafiltration volume , confirming the literature data ,33,34, pWe observed a nonsignificantly decreasing trend in ET-1 during dialysis. ET-1 is a potent vasoconstrictor, with elevated levels in HD patients. ET-1 has been described to increase during HD sessions in patients with dialysis-induced hypertension, to decrease in patients with dialysis-induced hypotension, and to be unchanged in patients whose BP did not change considerably during HD . In our The GCX is involved in the mediation of shear-induced nitric oxide releases. Excessive nitric oxide generation is involved in HD-associated hypotension , as the Hypercholesterolemia is known to compromise the integrity of the GCX ,17,26. GConvincing evidence has been provided for the role of hypervolemia in compromising the integrity of the vascular GCX ,17. In HAccording to the literature data, elevated SDC-1 is associated with decreased survival ,47. SDC-In our study, we divided our patients into two groups based on their PWV: the patients in the PWV < 12 m/s group were considered to be normal , and theAccording to our results, the endothelial GCX plays an important role in the regulation of vascular tone and central and peripheral BP, and eventually, it may have a considerable effect on the cardiovascular events and survival of HD patients. In these patients, several factors compromise the integrity of the GCX. We studied the effects of hypervolemia/the ultrafiltration volume, BP, hypercholesterolemia, and vascular rigidity. Increased vascular shear stress may be an important common mechanism, and SDC-1 is a valuable marker of GCX injury and repair.There are certain limitations in our study. As we considered this a “pilot” study, the patient number is small. The examinations should be extended to larger groups in the future. This is just a cross-sectional study. As vascular alterations have long-term effects on clinical endpoints, their effects should also be examined in long-term studies.SDC-1 and ET-1 contribute to the acute vascular changes observed during HD, and they correlate with the cardiovascular risk factors. It is important to restore the integrity of the GCX to decrease the very high cardiovascular risk of these patients. Further studies are needed to clarify further factors affecting vascular tone and the vascular GCX.


In hereditary angioedema (HAE) due to C1-inhibitor deficiency (C1-INH-HAE), bradykinin-mediated submucosal and/or subcutaneous angioedema dominates the clinical picture. The deficiency of C1-inhibitor can lead to the over-activation of the complement system. Complement plays an important role in all types of hypersensitivity reactions. On the other hand, during the degranulation of mast cells, heparin is also released amongst other substances. Heparin can activate the plasma kinin-kallikrein system, leading to bradykinin generation. These observations suggest a possible connection between C1-INH-HAE and mast cell-mediated hypersensitivity reactions.To assess the occurrence of hypersensitivity reactions in the Hungarian C1-INH-HAE population.Patients filled out a questionnaire of 112 questions, either online or on paper. The questions were about hypersensitivity and C1-INH-HAE symptoms, the relation between these 2, general health, and demographic data. The study protocol was approved by the institutional review board of Semmelweis University, Budapest, and informed consent was obtained from the participants.One hundred and six patients  responded, with 63.2% having hypersensitivity. Hypersensitivity was provoked by pollen in 25.5% of patients, by contact sensitivity in 22.6%, by food in 21.7%, by insect sting in 19.8%, by pet in 15.1%, by drug in 14.2%, by dust mite in 5.7%, and by mold in 1.9%. In 11 patients, hypersensitivity symptoms appeared after the diagnosis of C1-INH-HAE. Six hypersensitive patients experienced improvement in their symptoms; 42 remained the same, but none experienced worsening after the diagnosis of C1-INH-HAE. In 7.8% of the hypersensitive patients, a C1-INH-HAE attack worsened the hypersensitivity symptoms, while 15.7% of the hypersensitive patients experienced a C1-INH-HAE attack provoked by contact with the provoking factor.While 63.2% of our C1-INH-HAE patients have reported hypersensitivity symptoms, Eurostat's latest data puts the prevalence of self-reported allergies in Hungary at 19.3%. Since in our experience most Hungarian patients report hypersensitivity reactions as allergies, this may support a possible connection between the 2 diseases, but further molecular studies are needed. The angioedema is not associated with wheals or itching and does not respond to the conventional antihistamine, corticosteroid, or adrenaline treatment.,,In its most common form, HAE is caused by the reduced production (type I) or dysfunction (type II) of the C1 inhibitor protein (C1-INH).Hypersensitivity is defined by the World Allergy Organization's Nomenclature Review Committee as “objectively reproducible symptoms or signs initiated by exposure to a defined stimulus at a dose tolerated by normal persons” and allergy as “a hypersensitivity reaction initiated by specific immunologic mechanisms”, which can be divided into IgE-mediated allergy (Type I) or non-IgE-mediated allergy .14In C1-INH-HAE, the deficiency of C1-INH can lead to the over-activation of the complement system. Complement plays an important role in all types of hypersensitivity reactions. Complement components, for example, anaphylatoxins C3a and C5a, can activate mast cells in Type I hypersensitivity reactions. Thus, complement activation may synergize with classical IgE-mediated responses.21Based on these potential links in the pathomechanism of the 2 diseases, we aimed to investigate the occurrence of different types of hypersensitivity reactions in our Hungarian patients with HAE due to C1-INH-deficiency (C1-INH-HAE). In our study, besides assessing the prevalence of hypersensitivity reactions in general, our aim was to investigate the occurrence of different types of hypersensitivity in our patients as well as to examine the connection between HAE and hypersensitivity symptoms. When we started data collection, no similar survey was present in the literature. In the meantime, in 2022, a Swedish group published the results of their survey of 239 C1-INH-HAE patients. Investigating the comorbidities, they found that the prevalence of registered allergy, asthma, or atopic dermatitis was 2 times higher in their C1-INH-HAE patients than in the general Swedish population.We conducted a retrospective survey focusing on Hungarian C1-INH-HAE patients' hypersensitivity and HAE symptoms. The studied population consisted of diagnosed adult C1-INH-HAE patients who were monitored at the Hungarian Angioedema Center of Reference and Excellence (HU-ACARE). The diagnosis of C1-INH-HAE was established by the HU-ACARE's doctors 5 months to 44 years  prior to the survey by complement studies. We compiled a questionnaire of 112 questions that the participants could fill out either online (using Google Forms) or on paper. The participants were informed that the survey was voluntary and that their choice to participate or not would not affect their treatment (License number: 1067–5/2018/EÜIG). The study protocol was approved by the institutional review board, and informed consent was obtained from the participants in accordance with the Declaration of Helsinki.We asked 156 individuals between November 29, 2021, and March 1, 2022. Out of the 106 responders, 97 patients had type I, and 9 patients had type II C1-INH-HAE. The control group was the Hungarian adult population, based on published data from several Hungarian research groups and on the results of the 2019 European Health Examination Survey (EHES) published on the Eurostat website.The questionnaire  was in HIn our experience, patients don't understand the term “hypersensitivity” or tend to use the terms “allergy” and “hypersensitivity” as synonyms, usually preferring the use of “allergy”. Thus, to make it more understandable for our patients, in our questionnaire we used the term “allergy”.As a rule of thumb, we accepted all patient-reported symptoms as hypersensitivity, as we believe that they have only reported connections that either they could observe multiple times or that were considered by medical staff as allergies. However, medical professionals at our HU-ACARE have done a brief first analysis of the data based on the detailed answers of the patients. During this first analysis, we ruled out 2 patient-reported hypersensitivity reactions. Each exclusion and the reasons for them are shown in Our dataset mostly consisted of categorical variables that we characterized with percentage distribution. The only exceptions were the patients’ age and the time from the diagnosis of HAE, which were characterized by median, minimal, and maximal values and lower and upper quartile values. To determine if there was a connection between variables, a chi-squared test was used. All calculations were made using Microsoft Excel 2016 and GraphPad Prism 7.0 programs, and the significance level was set at 0.05.p-values 0.52, 0.79, and 0.68, respectively).As filling out the questionnaire was voluntary, 106 patients responded in total, amongst them 64 women and 42 men. Their median age was 46 years . Regarding place of residency, 20.8% (22/106) of the respondents lived in the capital, 19.8% (21/106) at county seats, 28.3% (30/106) in other cities, and 31.1% (33/106) in villages. Comparing these data to the whole Hungarian adult C1-INH-HAE population, no statistical difference was found in terms of sex, age, or place of residency (Some type of hypersensitivity symptom was reported by 63.2% of the responders (67/106) where a patient could, of course, have multiple hypersensitivities A. In thep = 0.822).Amongst the hypersensitive patients, 39/67 (58.2%) were female and 28/67 (41.8%) were male. Investigating the connection between the prevalence of hypersensitivity and the type of C1-INH-HAE, we found that out of our 97 patients with type I C1-INH-HAE, 61 had some type of hypersensitivity (62.9%), while this ratio was 66.6% (6/9) in the patients with type II C1-INH-HAE. There was no significant difference between the 2 groups , while 23.1% (24/106) have experienced HAE attacks provoked by food. The provoking drugs and food with the provoked symptoms are shown in We further investigated the connection between hypersensitivity and HAE symptoms triggered by drugs or food. Out of our 15 drug-hypersensitive patients, 4 (26.7%) have had HAE attacks provoked by drugs . Howeverp = 0.497).We were also interested in whether pollen-hypersensitive patients experienced more frequent HAE attacks during the “pollen season”, that we defined as the timespan from March to October. To answer this question, we looked up the symptom diary of the patients from 2017 to 2021. We calculated the monthly attack rate during the “winter season”  and the “pollen season” (March–October). We calculated the ratio of the monthly attack rates in “pollen season” and “winter season” and considered a ratio greater than 1 as a “more frequent attack rate”. We had 75 patients who filled out the questionnaire, and we also had data about their attacks available from at least 3 years prior to our interviews. Amongst these patients, 38.7% (29/75) had more frequent attacks during “pollen season”. This ratio was 45.0% (9/20) in the pollen-hypersensitive subgroup and 36.4% (20/55) in the non-pollen-hypersensitive subgroup. No statistical difference was found between the 2 groups in terms of HAE attack frequency in the pollen season  experienced the appearance of hypersensitivity symptoms after the diagnosis of HAE. The hypersensitivity symptoms disa-ppeared in 1.7% (1/60), got better in 8.3% (5/60), did not change in 70.0% (42/60), and no worsening of the hypersensitivity symptoms was experienced after the diagnosis of HAE by any of the patients (To the question patients C. Out ofpatients .Table 5CThe questions about the connection between HAE and hypersensitivity symptoms were answered by 51 patients. The appearance of HAE attacks worsened the hypersensitivity symptoms of 7.8% (4/51). Twice as many, 15.7% (8/51) have experienced at least 1 HAE attack triggered by hypersensitivity. The hypersensitivities and the provoked HAE attacks can be seen in First, we confirmed that the responders were representative of the Hungarian adult C1-INH-HAE population, as no significant connection could be observed between the group (study population or Hungarian C1-INH-HAE patients) and sex, age, or place of residency. In our C1-INH-HAE patient group, the prevalence of hypersensitivity reactions was rather high, with pollen hypersensitivity being the most prevalent form.In 2016, a questionnaire-based survey investigated ragweed allergy in 1000 Hungarian adults. Thirty-one percent of their respondents have had allergic rhinitis, and 22% of the respondents experienced allergic rhinitis during “ragweed season” (between August and September).para-phenylenediamine sensitivity. In this population, 5.76% were sensitive to this potent allergen found in hair dyes and henna tattoos.The second most common type of hypersensitivity in our patients was contact sensitivity. Between 1998 and 1999, a multi-center study was conducted in Hungary to assess the prevalence of fragrance contact sensitization. In this study, 8.2% of patients reacted to the fragrance mix skin test.Food hypersensitivity was also quite frequently reported in our C1-INH-HAE patients. In 2020, a survey about food allergies was conducted in a Hungarian center on 501 adults. Intolerance to biogenic amines was found in 50%, oral allergy syndrome was confirmed in 14%, and 1% was diagnosed with IgE-mediated food allergy. The most frequent provoking factors were fruits (40%), milk or dairy products (35%), and nuts or oilseeds (29%).To the best of our knowledge, no comprehensive data has been published in the last 20 years regarding the occurrence of allergy in the Hungarian adult population caused by animal dander, drugs, insect sting, dust mite, or mold. One-fifth of our patients reported insect sting hypersensitivity, with bees, mosquitos and wasps being the most frequent provoking factors and edema and erythema being the most frequent symptoms. We found that approximately one-seventh of our C1-INH-HAE patients had animal dander hypersensitivity, with cats and dogs being the most frequent provoking factors and sneezing and itching being the most frequent symptoms. Similarly, one-seventh of our patients had drug hypersensitivity, with β-lactam and other kinds of antibiotics being the most frequent provoking factors and rash being the most frequent symptom. One-twentieth of our patients had dust mite hypersensitivity, and we also had 2 patients who had mold hypersensitivity.,37One-tenth of our patients has experienced an HAE attack provoked by drugs . To the One-fifth of our patients has experienced an HAE attack provoked by food . Our fin,Regarding the relationship between C1-INH-HAE and hypersensitivity, we found only 1 patient with a drug that caused both and 5 patients with the same food causing both. In addition, we could not identify a statistical connection between pollen hypersensitivity and the HAE attack rate during the pollen season. Approximately one-sixth of hypersensitive patients have experienced HAE attacks provoked by a hypersensitivity reaction. These findings suggest that there is no strong statistical connection between hypersensitivity reactions and HAE attacks. However, on the level of individual patients, a connection between hypersensitivity and HAE symptoms could be observed. As the course of C1-INH-HAE shows high intra- and inter-individual variability,Most of our patients have not experienced any change in their hypersensitivity symptoms after the diagnosis of HAE, and those who felt their hypersensitivity symptoms had changed all reported milder symptoms of hypersensitivity after the diagnosis of HAE. We could think of 3 possible explanations: (a) after being diagnosed with HAE, the patients can get adequate therapy for their C1-INH-HAE symptoms, thus reducing stress levels, which leads to a more balanced lifestyle with less severe hypersensitivity symptoms; (b) after the diagnosis of C1-INH-HAE, the patients are referred to an allergologist-immunologist, who, aside from treating their HAE, can provide them with treatment for their hypersensitivity as well; and (c) prior to the diagnosis of C1-INH-HAE, edematous symptoms might have been misdiagnosed as allergy. However, a high rate of hypersensitive C1-INH-HAE patients reported that their hypersensitivity symptoms had started after the diagnosis of HAE, suggesting that the treatment of C1-INH-HAE by itself is not enough to prevent hypersensitivity reactions.The last EHES was carried out in 2019 in Hungary. In the questionnaire, 1 self-reported question on any kind of allergic disease was present. The result of the survey displayed on the Eurostat website reported the prevalence of allergy in the Hungarian population to be 19.3%.In 2022, a survey was conducted in Sweden, where the prevalence of registered allergy, asthma, or atopic dermatitis was found to be 2 times higher in the C1-INH-HAE population than in the general Swedish population.We found no significant difference in the prevalence of hypersensitivity reactions between our type I and type II C1-INH-HAE patients, which is reasonable, as the suggested common pathways of C1-INH-HAE and hypersensitivity through over-activation of the complement system and through heparin release are similarly affected in both cases.However, our study has its limitations. (a) As the questionnaire was self-reported, we had to rely on the patients' own definition of hypersensitivity, hypersensitivity symptoms, and HAE attacks; therefore, the data obtained this way might be biased by patients' different interpretation of symptoms. (b) As the filling of the questionnaire was voluntary, there is a chance that those who have hypersensitivity were more likely to respond than those who do not, potentially raising the perceived prevalence of hypersensitivity. (c) C1-INH-HAE is a rare disease with a prevalence of 1:50 000.To conclude, we investigated the occurrence of hypersensitivity reactions amongst Hungarian C1-INH-HAE patients followed at our HU-ACARE based on potential links in the pathomechanism of the 2 diseases. We found that the prevalence of hypersensitivity reactions in the C1-INH-HAE population is approximately 3 times higher than in the general Hungarian population, suggesting a connection between the 2 diseases. However, this study was only the first step of our research investigating the connection between allergy and C1-INH-HAE. Here, our aim was to investigate the clinical manifestations of hypersensitivity symptoms in our patients. We are aware that the information collected this way is biased and cannot be used to diagnose allergies in our patients. Therefore, we have planned and already launched the second part of our study, with 2 aims in mind. First, we would like to examine the prevalence of allergic diseases in our patients with the help of their previous medical records and blood samples collected in our biobank. Secondly, we are planning to measure selected mast cell mediators in the blood samples collected from our patients and to compare allergic and non-allergic HAE-patients’ samples.ACE, Angiotensin-converting enzyme; BK, Bradykinin; C1-INH, C1-inhibitor protein; C1-INH-HAE, Hereditary angioedema due to C1-inhibitor deficiency; EHES, European Health Examination Survey; FXII, Coagulation factor XII; HAE, Hereditary angioedema; HU-ACARE, Hungarian Angioedema Center of Reference and Excellence; MI, Methylisothiazolinone; PKKS, Plasma kinin-kallikrein system.10.13039/501100015269Hungarian Government and the 10.13039/501100000780European Union [grant number EFOP-3.6.3-VEKOP-16-2017-00009]; and the Hungarian National Research, Development And Innovation Office [grant number NKFI 124557]. The funding providers had no role in the research and/or the preparation of the manuscript.This work was supported by the The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.Concept and design: Hanga Réka HORVÁTH, Zsuzsanna BALLA, Henriette FARKAS.Collection of data: Hanga Réka HORVÁTH, Dávid SZILÁGYI, Beáta VISY, Henriette FARKAS.Data analysis: Hanga Réka HORVÁTH, Noémi ANDRÁSI, Zsuzsanna BALLA, Beáta VISY, Henriette FARKAS.Manuscript writing: All authors.Final approval of manuscript: All authors.The study protocol was approved by the institutional review board of Semmelweis University, Budapest, and informed consent was obtained from the participants in accordance with the Declaration of Helsinki. Before completing the survey, the participants were informed that the survey was voluntary and that their choice to participate or not would not affect their treatment (Licence number: 1067–5/2018/EÜIG).All authors give their consent to the publication of this work.The authors hereby declare that this manuscript has not been published previously (except in the form of an abstract and academic thesis), that it is not under consideration for publication elsewhere, that its publication is approved by all authors and tacitly by the responsible authorities, and that, if accepted, it will not be published elsewhere in the same form, in English or in any other language, including electronically without the written consent of the copyright holder.Hanga Réka HORVÁTH has received travel grants from Takeda.Dávid SZILÁGYI has no conflict of interest to declare.Noémi ANDRÁSI has no conflict of interest to declare.Zsuzsanna BALLA has participated in clinical trials of CSL Behring, Pharvaris and Takeda.Beáta VISY has participated in clinical trials of Kalvista, CSL Behring, Pharvaris and Takeda.Henriette FARKAS has received research grants from CSL Behring, Takeda and Pharming and served as an advisor for these companies and Kalvista and Biocryst, and has participated in clinical trials/registries for BioCryst, CSL Behring, Pharming, Kalvista, Pharvaris and Takeda.


The unprecedented scale of the SARS-CoV-2 pandemic has driven considerable investigation into novel antiviral treatments since effective vaccination strategies cannot completely eradicate the virus. Apitherapy describes the medicinal use of bee venom, which may be an effective treatment against SARS-CoV-2 infection. Bee venom contains chemicals that are antimicrobial and stimulate the immune system to counteract viral load. The present review focuses on the use of bee venom as a possible treatment for COVID-19 and reviews studies on the pharmacodynamics of bee venom. The competitive co-evolution of hosts and pathogens has always proved to be a significant driving force for scientific research. SARS-CoV-2, the virus causing COVID-19, emerged in the Wuhan province in China in 2019 ,4. COVIDApiculture or the rearing of honey bees is a fairly common practice in almost all countries, including India. It provides a cheap, fast and reliable way of utilizing the products of honey bees, which include propolis, royal jellies, bee wax and honey. Bee venom (BV) is another very important component of honey bees. The venom in bees is stored in venom sacs located on the posterior side of the abdomen and it is released via the sting apparatus which punctures the skin as a bee stings . The relThe venom of honey bees is a complex cocktail of several biomolecules and thus the functionality cannot be attributed to any single component. Bee venom constituents include different enzymes like phospholipaseA2 (PLA2), phospholipase B, hyaluronidases, acid phosphatases, acid phosphodiesterases, a-D-glucosidases and lysophospholipases. The non-enzymatic peptides in bee venom include melittin, apamin, mast cell degranulating peptide, secapine, pamine, minimine, procamine A, B, protease inhibitor, tertiapine, cardiopep and adolapin . Bee venThe deleterious effects of the chemical constituents of bee venom include prominent inflammation, allergic reactions to protease inhibitors, anaphylactic responses and death . HoweverA. dorsata, A. mellifera, A. florea and A. cerana were reported to be 95.8 ± 3.2%, 76.5 ± 1.9%, 66.3 ± 8.6% and 56.8 ± 1.8%, respectively [A. dorsata being the largest of the different species has the largest venom gland and venom sac, followed by A. cerana, A. mellifera and A. florea. Besides, it has also been reported that the concentrations of constituent lipid, protein, carbohydrate and alkaline phosphatase are usually the most in A. cerana, followed by A. mellifera and A. florea [A. cerana exhibited the most significant antimicrobial activity against Gram-positive bacteria compared with Gram-negative bacteria and fungus, followed by A. mellifera, A. florea and A. dorsata [Since there is significant species diversity among honey bees, the composition of the venom also varies accordingly in different species. These differences might be an adaptive response according to the habitats the bees reside in. The concentration and structural variability of melittin is exhibited in different species of honey bees . Melittiectively . The bod. florea . Experim dorsata . It has A properly directed low concentration and diluted BV has been shown to trigger a wide range of anti-inflammatory responses . Hence, Apis mellifera) venom has been used since the ancient Egyptian era (4000 BC) [The Western honey bee's (4000 BC) . The ben4000 BC) . The ven4000 BC) , antimic4000 BC) ,48–50 an4000 BC) ,51. Hone4000 BC) . Melitti4000 BC) . Melitti4000 BC) . The rem4000 BC) . Of all 4000 BC) .The medical use of BV was established during the times of Hippocrates when the toxin was used to treat conditions of arthritis and joint pain . There hThe administration of BV has also been associated with the differentiation of regulatory T cells . HoweverSARS-CoV-2 belongs to the β genus of the family of coronaviruses which has a protective protein covering enclosing the viral genome. There are four main types of structural proteins in SARS-CoV-2, which are categorized as membrane, spike, nucleocapsid and envelope protein. The membrane of the virus contains 3–4 glycoproteins , which aBee venom might be an interesting form of medical intervention to prevent the severe form of COVID-19. The direct association of BV components with ACE-2 substantially points to the direct connection for utilizing BV against SARS-CoV-2, which initiates its cellular entry via the ACE-2 receptor . The prein vivo. This presentation of antigens stimulates a T-cell (CD8+ve) mediated immune response which promises to manifest as a possible anti-viral and anti-tumor vaccine model in the future. Vaccines from BV and DCs  are used for immunization against viruses such as cytomegalovirus and for suppression of tumors [Controlled and low doses of bee venom have shown significant anti-inflammatory responses which have been utilized as a speculative therapeutic approach in diabetes, rheumatoid arthritis (RA), cardiovascular diseases, obesity, asthma, skin diseases and neurodegenerative disorders like Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis ,75. Thisf tumors ,78. Alumf tumors ,80. CombMelittin-loaded nano-particle formulations might be the future approach to neutralize SARS-CoV-2 and prevent it from culminating into COVID-19. There also can be gel formulations in the form of nasal sprays containing melittin-loaded nano-formulations which might stop the spread of the virus from the nasopharyngeal region. An interesting survey report of 5115 beekeepers in the Hubei province of China, among which 723 beekeepers were directly from Wuhan, which was the epicenter of the COVID-19 pandemic symptoms, showed no COVID-19 symptoms . A similSudden outbreak of the novel SARS-CoV-2 virus and its severe consequences on human health.Medications and vaccines administered to date are not completely beneficial against the virus.There is a need for complementary medicine that can be obtained by utilizing the antimicrobial properties of the constituents of bee venom.Bee venom is a complex cocktail of peptides, enzymes, amines, etc.Melittin is the predominant peptide that has shown antimicrobial effects in different experimental setups.Bee venom definitely exerts severe allergic responses and inflammation but only at high doses.The venom content and its chemistry differ among different species of honey bees.Melittin is the predominant peptide found in the venom of almost all species of honey bees.The possibility of using bee venom as complementary medicine against COVID-19.Low and controlled dosing of bee venom or its components can be of significant medical importance against SARS-CoV-2.Melittin has the ability to induce pore formation in the cell membrane and also stimulates the immune system by triggering the release of interferons.These properties of melittin have been utilized against Herpes virus, H1NI influenza, etc., and can also be of immense significance against SARS-CoV-2.Melittin is the water-soluble predominant compound in BV and hence the administration via injection becomes relatively less complex.Melittin has been known to exhibit cell lysis properties by inflicting pore formation on the membrane of the cell.The ability of the compound to inflict cell lysis has also been effectively utilized against Herpes simplex virus, H1N1 Influenza A virus.Melittin-loaded nano-particles intravenously has successfully killed pancreatic lesions in K14-HPV16 mice with squamous dysplasia and carcinoma containing human papillomavirus (HPV) transgenic elements.Melittin might be the answer that we have been trying to search for the past year to achieve a potential therapeutic strategy toward SARS-CoV-2.Melittin has proven to be beneficial in real-life case histories wherein beekeepers involved in apiculture did not suffer the consequences of SARS-CoV-2 in China. Similar results were also obtained among apitherapists.Nasal formulations containing nano-formulations loaded with melittin might be a futuristic strategy to employ against restricting the SARS-CoV-2 to the nasal passage only.Apart from melittin alone, phospholipase A2 has also been scientifically proven to induce T-cell-mediated immune response by targeting dendritic cells for antigenic processing and presentation.Bee venom-derived anti-viral or anti-microbial vaccines are the future therapeutic strategy against most viral antigens.Melittin and phospholipase A2 might be the replacement for aluminum hydroxide as an adjuvant utilized in immune therapy in COVID-19.


Plasmodium species is a prominent public health concern worldwide, and the infection of a malarial parasite is transmitted to humans through the saliva of female Anopheles mosquitoes. Plasmodium invasion is a rapid and complex process. A critical step in the blood-stage infection of malarial parasites is the adhesion of merozoites to red blood cells (RBCs), which involves interactions between parasite ligands and receptors. The present study aimed to investigate a previously uncharacterized protein, PbMAP1 (encoded by PBANKA_1425900), which facilitates Plasmodium berghei ANKA (PbANKA) merozoite attachment and invasion via the heparan sulfate receptor.Malaria caused by PbMAP1 protein expression was investigated at the asexual blood stage, and its specific binding activity to both heparan sulfate and RBCs was analyzed using western blotting, immunofluorescence, and flow cytometry. Furthermore, a PbMAP1-knockout parasitic strain was established using the double-crossover method to investigate its pathogenicity in mice.PbMAP1 protein, primarily localized to the P. berghei membrane at the merozoite stage, is involved in binding to heparan sulfate-like receptor on RBC surface of during merozoite invasion. Furthermore, mice immunized with the PbMAP1 protein or passively immunized with sera from PbMAP1-immunized mice exhibited increased immunity against lethal challenge. The PbMAP1-knockout parasite exhibited reduced pathogenicity.The PbMAP1 is involved in the binding of P. berghei to heparan sulfate-like receptors on RBC surface during merozoite invasion.The online version contains supplementary material available at 10.1186/s13071-023-05896-w. Plasmodium parasites in red blood cells (RBCs) and the rupture of infected RBCs (iRBCs) are responsible for most symptoms of malaria  or [- X-B-B-B-X-X-B-X-], where B is the basic residue of basic amino acids, such as lysine, arginine, and histidine, and X is a hydrophilic amino acid residue . Previouα region . In the PbMAP1 is expressed on the surface of P. berghei. As a surface protein, PbMAP1 may be involved in the interaction between the parasite and RBC surface receptors. Based on three-dimensional (3D) structural modeling of PbMAP1 and its heparin-binding motifs, we expressed and purified the recombinant protein GST-PbMAP1 in Escherichia coli and used the GST protein as the negative control for functional verification. Heparin-binding and competitive inhibition experiments showed that GST-PbMAP1 could bind to heparin in a concentration-dependent manner (Fig. PbMAP1 protein could bind to the surface of RBCs (Fig. PbMAP1 may be involved in binding to HS-like receptor on the RBC surface.Specifically, our IFA and electron microscopy demonstrated that ner Fig. . IFA, weensional D structuPbMAP1 was knocked out to assess its function. We found that the pathogenicity of the ΔPbMAP1 strain in mice was attenuated compared to that of the WT strain (Fig. PbMAP1-knockout strain further demonstrated the role of the PbMAP1 protein in the binding to RBCs. In addition, we observed that mice immunized with PbMAP1-specific peptides achieved immune protection (Fig. PbMAP1 may play important roles in the binding and invasion of P. berghei to RBCs. Interestingly, we found that with the continuous replication of the parasites, the infectivity of the ΔPbMAP1 parasite seemed recovered. Therefore, we speculated that some ways of compensation may occur. We collected WT and ΔPbMAP1 strains and searched for genes with significant differences in transcript levels after PbMAP1 knockout using qRT-PCR. The transcript level of MAEBL was surprisingly found significantly upregulated, but the transcript levels of 10 other merozoite genes were significantly downregulated. These results confirmed that PbMAP1 knockout affected MAEBL expression (Fig. Furthermore, ain Fig. . The useion Fig. . Therefoion Fig. .PbMAP1 strain can confer immune protection in mice, we examined changes in cytokine levels after immunizing with the ΔPbMAP1 strains. Levels of IFN-γ, TNF-α, IL-6, and IL-10, which are important for the activation of protective immunity, were significantly increased at the early stage of ΔPbMAP1 infection. Therefore, PbMAP1 expression is likely beneficial to the parasite by preventing protective immune responses in the host.To assess the potential mechanism through which injection with the ΔPbMAP1 is involved in the binding of P. berghei to the HS receptor on RBC surface. The absence of PbMAP1 is presumably deleterious to the parasite, as it evokes a specific immune response in the host. Our findings revealed the interaction between PbMAP1, a novel P. berghei surface protein, and HS-like receptor during invasion. Simultaneously, these findings can be useful approaches for deep characterization of P. falciparum proteins in human malaria.The present study showed that the surface protein Additional file 1: Table S1. Primer sequences referred to in this study.


Escherichia coli (AIEC) is strongly associated with the pathogenesis of Crohn’s disease (CD). However, no molecular markers currently exist for AIEC identification. This study aimed to identify differentially expressed genes (DEGs) between AIEC and non-AIEC strains that may contribute to AIEC pathogenicity and to evaluate their utility as molecular markers.Adherent-invasive Comparative transcriptomics was performed on two closely related AIEC/non-AIEC strain pairs during Intestine-407 cell infection. DEGs were quantified by RT-qPCR in the same RNA extracts, as well as in 14 AIEC and 23 non-AIEC strains to validate the results across a diverse strain collection. Binary logistical regression was performed to identify DEGs whose quantification could be used as AIEC biomarkers.wzb and cueR gene expression were proposed with an accuracy of ≥ 85% in our strain collection.Comparative transcriptomics revealed 67 differences in expression between the two phenotypes in the strain pairs, 50 of which (81.97%) were corroborated by RT-qPCR. When explored in the whole strain collection, 29 DEGs were differentially expressed between AIEC and non-AIEC phenotypes , and 42 genes between the supernatant fraction of infected cell cultures and the cellular fraction containing adhered and intracellular bacteria . Notably, six DEGs detected in the strain collection were implicated in arginine biosynthesis and five in colanic acid synthesis. Furthermore, two biomarkers based on This is the first transcriptomic study conducted using AIEC-infected cell cultures. We have identified several genes that may be involved in AIEC pathogenicity, two of which are putative biomarkers for identification. AIEC strains are distinct from other E. coli intestinal pathotypes because the typical virulence genes of invasive pathotypes are absent in AIEC (E. coli (ExPEC) (Adherent-invasive ase (CD) . A highease (CD) ; and AIEase (CD) . The AIE in AIEC . Convers (ExPEC) .E. coli strains from different geographical regions and phylogenetic backgrounds.Currently, the genetic elements and mechanisms underlying the AIEC phenotype are not fully understood, and no molecular markers exist for its identification. Detection still relies on phenotypic assays on infected cell cultures, making it time-consuming and poorly standardized. Previous studies using molecular methods to identify genes associated with AIEC and widely distributed within the pathotype were unsuccessful . ComparaPreviously, we proposed using three single nucleotide polymorphisms (SNPs) that, when combined, achieved an accuracy of 81% for AIEC detection . HoweverE. coli strain K-12 gene expression in the presence and absence of bile salts. The results showed that bile salts induced over-expression of six genes involved in ethanolamine utilization in LF82 but not K-12  and the eukaryotic cell fractions containing adhered and invading bacteria (A/I fraction) of two AIEC/non-AIEC strain pairs. The AIEC07/ECG04 strain pair was obtained from the ileum of a control subject and the AIEC17/ECG28 strain pair was isolated from the colon of a CD patient in a previous study . SequencComparative transcriptomics between the two AIEC strains and their non-AIEC counterparts revealed a total of 67 differences in gene expression, of which 48 were under-expressed in AIEC (25 in the A/I fraction and 23 in the SN fraction) and 19 were over-expressed (12 in the A/I fraction and 7 in the SN fraction) Figure 1Regarding the gene expression levels, total fragments per kilobase of transcript per million mapped reads (FPKM) values were lower than 1000 in 73.13% of the studied DEGs. A group of 13 genes had higher total FPKM values, of which 12 were classified as tRNA in the functional categories, and one was classified as sRNA  values obtained from RT-qPCR  (Thirty-three out of the 42 genetic AIEC under-expressions (78.57%) and 17 of the 19 genetic over-expressions (89.47%) detected in RNA-seq were confirmed by RT-qPCR as under- and over-expressions, respectively Figure 32 RTA values obtained from the two types of samples were compared to determine the effects of purification.Non-purified samples of the strain pairs were also analyzed by RT-qPCR. These samples were obtained before the purification steps of the MICROBEnrich kit and the Ribo-Zero Magnetic Gold kit (epidemiology). The differences in gene expression between purified and non-purified samples were investigated. Gene expression differences were quantified by RT-qPCR in purified and non-purified RNA extracts from the AIEC/non-AIEC strain pairs. The log2 RTA values were found between purified and non-purified samples of AIEC SN, non-AIEC SN, and AIEC A/I , whereas no statistically significant differences were found in non-AIEC A/I samples   , and 10 Since differences in gene expression can only be compared for samples in which the genes have been amplified, a preliminary analysis was performed to study the frequency of gene amplification in AIEC and non-AIEC samples. Non-amplification can result from the absence of a gene in a given strain or an expression level below the detection limit. Eight genes  were amplified more frequently in AIEC strains than in non-AIEC strains  Figure 4A concordance was found between the differential gene expression data obtained by RNA-seq and RT-qPCR data obtained from the entire strain collection Figure 5Statistically significant differences  between AIEC and non-AIEC strains were found for 29 genes (40.27%). Most of them (N = 26) were detected in the SN fraction, one in A/I (1184), and two (0511 and 1198) were found in both fractions   were studied in order to determine if this differential expression could be related with acid resistance. The genes adiA and adiC belong to the arginine-dependent acid resistance system, responsible of the arginine decarboxylation to agmatine and the pH-dependent arginine:agmatine transport. The gene potE codifies for the ornithine:putrescine antiporter that belongs to the ornithine-dependent acid resistance system  and 3172b (yjjB) were over-expressed in AIEC strains in SN compared to non-AIEC strains. The tdcA gene encodes for a transcriptional regulator that activates the tdc operon and is related to serine degradation, while yjjB is a putative serine/threonine exporter.Moreover, the genes 0036 . In non-AIEC strains, wzc, wcaE, and gmd were over-expressed at the A/I fraction compared to the SN, while wzb was over-expressed in AIEC SN compared to AIEC A/I and non-AIEC SN. However, no significant differences were observed, and more strains could be included to verify these findings.Furthermore, five genes presented functions related to colanic acid biosynthesis and export , all under-expressed in the SN fraction compared to A/I in non-AIEC strains. Furthermore, fimH was included in this analysis, and an increased expression in AIEC SN was observed compared to non-AIEC SN, with a mean of log2 RTA of 0.49 and -2.13, respectively .Three genes were related to type 1 fimbriae . They all presented statistically significant differences  patterns, reducing the probability of detecting DEGs inherent to the phylogenetic origin of the strains and increasing the likelihood of detecting genes specifically related to the AIEC phenotype. Gene expression was analyzed during IEC infection to ensure that genes involved in adhesion, invasion, and intracellular replication were expressed. RT-qPCR was used to quantify the expression level of DEGs in a larger collection of AIEC and non-AIEC strains from different phylogroups to validate the results. This approach identified genes associated with the AIEC phenotype and putative molecular markers that could be used for AIEC screening.3.1In the strain collection, 29 genes were differentially expressed between AIEC and non-AIEC strains. Surprisingly, most of these genes (28 out of 29) were found in the SN fraction before adhesion or invasion occurred. This unexpected result may be because the proportion of non-AIEC bacteria adhered to the IECs may have a similar gene expression pattern to AIEC adhered to IECs, and adhered bacteria are much more abundant than intracellular bacteria in these samples. However, the 42 genes differentially expressed between the SN and A/I fractions in the strain collection may be a source of new virulence factors involved in cell adhesion and invasion. Interestingly, 40 of these 42 genes were detected in non-AIEC strains, while only 11 were detected as differentially expressed in AIEC strains. This unexpected result may be explained by the fact that these virulence factors are constitutive genes or present some basal expression in AIEC strains, while in non-AIEC strains, they are only expressed after entering into contact with IECs.argI, argF, argC, argB, and argH, which are regulated by ArgR, a repressor that acts in the presence of arginine. In the strain collection, these genes were over-expressed in AIEC in the SN fraction. When analyzed individually, these genes were over-expressed in 77.8%, 54.5%, 90.0%, 90.0%, and 100.0% of the AIEC strains, respectively. Interestingly, in a study by Elhenawy et al., in vitro and in vivo gene expression of the AIEC strain NRG857c was compared by comparative transcriptomics, and it was shown that genes involved in the arginine biosynthesis pathway are upregulated in the intestinal environment of animal models. Additionally, these genes are involved in AIEC survival in vivo and are essential for robust gut colonization, as mutants of argBCGH genes were significantly depleted from the murine intestine  in the A/I fraction compared to non-AIEC strains. Colanic acid is involved in developing mature biofilms but not in bacterial adhesion to different biotic and abiotic surfaces  were over-expressed in AIEC compared to non-AIEC strains in both fractions, although statistically significant differences were only observed in the SN fraction. When comparing the SN and A/I fractions, similar gene expressions were observed in AIEC strains, while in non-AIEC strains, an over-expression in the A/I fraction was seen. It is known that the expression of the fim operon is a phase variable and depends on the orientation of an invertible element that contains the promoter. Consistent with our findings, it has been observed that non-AIEC strains mainly present the OFF phase, avoiding the expression of the fim operon  located on the apical surface of epithelial cells. CEACAM6 expression is upregulated in CD patients, leading to increased interaction   . However strains , 2020. I3.3Our study has increased our knowledge of the molecular mechanisms involved in AIEC pathogenicity. Gene expression levels of AIEC and non-AIEC strains in both SN and A/I fractions have revealed genes and pathways that may be involved in the adhesion and invasion of AIEC to IECs. These results provide a foundation for future investigations of therapeutic targets against AIEC colonization. Furthermore, we have proposed two genes as biomarkers to identify AIEC strains based on quantifying their expression levels. Subsequent studies to validate the distribution of these biomarkers in a geographically diverse collection of AIEC strains and their usefulness in clinical samples are of great interest.44.1Two previously isolated and characterized AIEC/non-AIEC strain pairs were used in this study. These pairs correspond to strains with similar genome structure, gene content, identical PFGE patterns, and sequence types, but with a different AIEC phenotype . The AIE2 at 37°C with EMEM medium  supplemented with 10% fetal bovine serum , 1% antibiotic-antimycotic , 1% glutamine , 1% minimum essential medium non-essential amino acids , and 1% minimum essential medium vitamins .The Intestine-407 epithelial cell line  was maintained in an atmosphere containing 5% CO4.27 I-407 cells and incubated for 20 h. Cells were washed twice with 10 mL of phosphate-buffered saline , and a cell culture medium composed of EMEM  with 10% heat-deactivated FBS  was added. Infection of cells was performed at a multiplicity of infection (MOI) of 100, with bacteria growing exponentially in LB broth (OD600 = 0.625). After 4 h of incubation at 37°C with 5% CO2, the supernatant (SN fraction) was collected, and the eukaryotic cells containing adhered and invading bacteria (A/I fraction) were washed twice with the cell culture medium and recovered with a scraper. Both fractions were immediately kept on ice and centrifuged at 4°C for 10 min at 3000 × g. The supernatant was discarded, and the pellet was washed with 0.5 mL of PBS.T75 flasks were seeded with 2 × 104.3Both the SN and A/I fractions were processed equally. The TRIzol Max Bacterial Isolation kit with Max Bacterial Enhancement reagent  was used to obtain total RNA, following the manufacturer’s instructions with the following modifications: an incubation of 15 min at room temperature was done after chloroform addition, and overnight precipitation at -20°C was performed after the addition of isopropanol. Subsequently, a DNase I treatment  was applied using 1 U of DNase for 5 µg of RNA. The samples obtained at this point were named ‘non-purified’ samples.To begin RNA purification, mRNA containing a polyA tail, 18S and 28S eukaryote rRNAs were removed with the MICROBEnrich kit  using Oligo MagBeads. For all samples, at least 25 µg of total RNA in a maximum of 30 µl were treated with this kit. If the RNA concentration was insufficient, an additional precipitation step was carried out as recommended in the kit instructions. The Ribo-Zero Magnetic Gold kit (epidemiology)  was used on 2.5 µg of sample RNA to remove all rRNAs and a part of miRNA and tRNA, obtaining mainly purified prokaryotic mRNA. The samples obtained at this point were named ‘purified’ samples.RNA quantification was done before and after each kit with a Nanodrop spectrophotometer and a Qubit Fluorometer with RNA HS Assay kit . Additionally, RNA quality was verified with a denaturing agarose gel and/or the Total Prokaryotic RNA program of the Agilent RNA 6000 Nano Chip Bioanalyzer .4.4The TruSeq Stranded mRNA method  was used for cDNA synthesis. Two SN samples and one A/I sample were sequenced for each strain by Illumina Miseq. The sequencing depth varied according to the fraction: 10 million reads for samples mainly composed of bacteria (SN fraction) and 200 million reads for samples from infected cells (A/I fraction).Sequence reads were analyzed using FastQC, trimmed,4.5E. coli strain collection to determine whether these expression differences were widely distributed among AIEC strains and could be used as molecular biomarkers for AIEC identification. The strain collection included 13 AIEC and 22 non-AIEC strains isolated from ileum and colon biopsies of patients with clinically confirmed inflammatory bowel disease and from control subjects . However, the samples in this part of the analysis were not treated with the MICROBEnrich or Ribo-Zero Magnetic Gold kit, so non-purified RNA extracts were obtained from the SN and A/I fractions. The strain pairs used for RNA-seq were also included in this part of the analysis, so gene expression levels could be compared between purified and non-purified samples for the AIEC17, AIEC07, ECG28, and ECG04 strains.4.6The previously identified DEGs were quantified in the same sequenced samples to validate RNA-seq results, as well as in non-purified strain pair extracts and a collection of AIEC and non-AIEC strains.First, 1.5 µg of RNA was reverse transcribed using the High-Capacity cDNA Reverse Transcription Kit  and the RNase Inhibitor  in a final volume of 20 µL. Since the SN samples of the strain pairs were studied in duplicate, they were mixed equally before cDNA synthesis.in silico PCR amplifications at http://insilico.ehu.es/PCR/. No amplification of the human genome was verified through two databases (UCSC In-Silico PCR and BIOTECH In Silico PCR).Gene-specific primers were designed with the support of Primer3 0.4.0 . The Fluidigm loading kit-10 IFCs (BMK-M10-96.96) was used following the manufacturer’s instructions. Before amplification, a cycle of 10 min at 95°C followed by 16 cycles of 15 s at 95°C and 4 min at 60°C was performed. Three technical replicates were analyzed for each sample.E = 10 (-1/slope))  to determine the calibration curve and amplification efficiency (/slope)) . To norming gene .The relative transcript abundance (RTA) of mRNA for each DEG was calculated with the equation :2 RTA  indicate over-expression in AIEC, while positive log2 RTA  indicates under-expression in AIEC. In the strain collection, LF82 A/I sample was used as reference to calculate RTA values. Therefore, negative log2 RTA a lower expression of the sample compared with LF82 A/I while positive log2 RTA indicates a higher expression.The respective AIEC strain was applied as a reference in the study of the strain pairs. Since RTA was calculated for the non-AIEC strain using the respective AIEC as a reference in the strain pairs, negative log4.72 fold change values obtained from the comparative transcriptomics of RNA-seq data and the log2 RTA values obtained from Fluidigm analysis was determined using Spearman’s correlation. The effect of mRNA purification on gene expression ratio in RNA extracts was studied by comparing log2 RTA values of purified and non-purified samples of the strain pairs using a paired t-test.The level of correlation between the log2 RTA values was compared between the AIEC and the non-AIEC groups for each DEG. Previously, a Shapiro-Wilk test was performed to assess if the variables were normally distributed, and the Levene test was used to assess homoscedasticity. Gene expression levels were also compared between SN and A/I fractions using a paired t-test for each phenotype. A chi-squared test was used for each gene to compare the detection frequency between AIEC and non-AIEC strains.To identify which DEGs were differentially expressed between AIEC and non-AIEC phenotypes, gene expression levels were compared between AIEC and non-AIEC strains of the whole strain collection using a Mann-Whitney U test for non-parametric data or a t-test for parametric data. In this analysis, the mean of log2 RTA values obtained. Before the analysis, all missing values were replaced. Samples with no amplification of the DEG and a CtgapA< 18 were considered to have a correct RNA concentration but an undetectable gene expression. To give a value for these samples, the log2 RTA was calculated by replacing the no-amplified Ct with 25, which is the detection limit of Fluidigm. Samples with a CtgapA > 18 were considered not concentrated enough to ensure accurate quantification of genes with low expression levels. In this case, the missing values were substituted with the mean value of log2 RTA for each DEG according to pathotype to avoid bias. Only genes amplified in >60% of strains were analyzed, and those with high predictive potential for the AIEC phenotype were selected based on their sensitivity, specificity, and accuracy.To determine which DEGs could be used to predict the AIEC phenotype, a binary logistic regression analysis was performed for each DEG individually using the logAll statistical analyses were performed with either SPSS  or GraphPad . For all analyses, a p-value ≤ 0.05 was considered statistically significant.https://doi.org/10.34810/data699 and https://doi.org/10.34810/data698.The raw data supporting the conclusions of this article are available from the Catalan Open Research Area (CORA) repository, with the following DOIs: MM-M designed the study. QB-R, CC-F, and ML-S obtained the data. QB-R performed the statistical analysis. QB-R, CC-F, ML-S, and MM-M performed data interpretation. QB-R drafted the manuscript. CC-F, ML-S, and MM-M revised the manuscript. MM-M obtained funding. All authors contributed to the article and approved the submitted version.


The source code and experimental data is available at https://github.com/Xiaohong-source/6mA-stackingCV.DNA N6-adenine methylation  plays a key regulating role in the cellular processes. Precisely recognizing 6mA sites is of importance to further explore its biological functions. Although there are many developed computational methods for 6mA site prediction over the past decades, there is a large root left to improve. We presented a cross validation-based stacking ensemble model for 6mA site prediction, called 6mA-StackingCV. The 6mA-StackingCV is a type of meta-learning algorithm, which uses output of cross validation as input to the final classifier. The 6mA-StackingCV reached the state of the art performances in the Rosaceae independent test. Extensive tests demonstrated the stability and the flexibility of the 6mA-StackingCV. We implemented the 6mA-StackingCV as a user-friendly web application, which allows one to restrictively choose representations or learning algorithms. This application is freely available at  DNA methylation is one of epigenetic modifications, which refers to a chemical process where the methyl groups are attached to the DNA nucleotide residues. So far, it has been reported that DNA methylation occurs only at two nucleotides: adenine and cytosine. The DNA methylation at the cytosine includes N5-methylcytosine (5mC) , HydroxyAccurately identifying the DNA 6mA sites is of great importance for exploring its mechanism and function. Many techniques have been developed to identify 6mA modification, which can be grouped into two categories: wet and dry methodologies. The wet methodology is to use physic or chemistry-based methods to detect 6mA sites, which includes liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS), single-molecule real-time (SMRT) sequencing, 6mA-immunoprecipitation sequencing (6mA-IPseq), restriction enzyme-based sequencing (6mA-REseq) , 16. TheThe representations of DNA N6-methyladenine and the machine learning algorithms are two key factors to determine the predictive accuracy. The machine learning algorithms are classified into the traditional machine learning and the deep learning. The former includes support vector machine (SVM), logistic regression, random forest, multilayer perceptron, and naïve Bayes, which exhibited high performance especially for small samples. The deep learning is actually a deep multiple-layer neural network with specific architectures. Besides the traditional fully-connected network, some network architectures have been proposed, such as long short term memory (LSTM) , convoluBesides one-hot encoding and EIIP, we computed five categories of popular representation of nucleotide sequences, i.e., the NCP, kmer (k = 3), NAC, ENAC, and ANF. The NCP is similar to One-hot encoding. Difference between them lies that the NCP employed chemical properties . AccordiThat’s to say, A is encoded into , C into , G into , and T into . Kmer refers to the occurrence frequencies of k continuous nucleotides. The NAC refers to the occurrence frequency of single nucleotide. ENAC  is defin The ANF , 44 is cThe ANF reflects distribution of positions and nucleotides. A sequence with N nucleotide residues has N ANF features.We used XGBoost as the learning algorithm and employed hold out to examine performance of feature’ distinguishing between 6mA and non-6mA. The hold out is to split the training set into two parts, one for training and another for validation. We set the ratio of splitting the training set as 8 to 2. We repeated hold out test five times. The average performance and 95% confidence intervals were listed in Table We used hold-out to optimize the model. We employed the backward searching strategy. Firstly, we used six popular classifiers  in the first layer, and fixed support vector machine in the second layer. We conducted hold-out test. Then, we removed a classifier each time. The increased ACC meant that the removed classifier contributed negatively to the performance, and was not used in the subsequent experiments. On the contrary, the decreased ACC meant that the removed classifier contributed positively to the performance, and was preserved in the subsequent experiments. We performed above two steps repeatedly until there was not new combination. The performances of all combinations of classifiers were listed in Table After fixing XGBoost, Gradient boosting and Lightgbm in the first layers, we optimized the second layer. We placed random forest, logistic regression, decision tree, XGBoost, Gradient boosting, LightGBM, and SVM in the second layer respectively, and then conducted hold-out test. The performance was listed in Table https://github.com/YuXuan-Glasgow/SNN6mA [With development of artificial intelligence, more and more attentions have been paid to computational methods for 6mA identification. Over the past decades, more than ten computational methods have been developed to predict 6mA sites. We compared the 6mA-StackingCV with these existing methods by three independent tests. One independent test was to test 6mA-StackingCV for ability to predict Rosaceae 6mA sites, and other two independent tests are to test 6mA-StackingCV for ability to predict 6mA sites across species. As shown in Table w/SNN6mA . One wasw/SNN6mA , by 0.02w/SNN6mA , by 0.04w/SNN6mA  by 0.033w/SNN6mA  for the We further tested ability for the 6mA-StackingCV to predict 6mA site across species. As shown in Table We used the SHAP (SHapley Additive exPlanations)  to exploWe proposed a cross validation-based stacking ensemble model 6mA-StackingCV for 6mA site prediction. The 6mA-StackingCV reached state of the art performance in the Rosaceae and was superior to i6mA-Fuse_RC , i6mA-Prhttp://www.biolscience.cn/6mA-stackingCV/. The 6mA-StackingCV was easy to use.We presented an improved stacking ensemble model for predicting DNA N6-methyladenine site. The 6mA-StackingCV was superior to the state of the art methods for Rosaceae, and competitive with those for Arabidopsis and Rice. The 6mA-StackingCV was robust and flexible, benefiting from using cross validation to construct the classifiers. We implemented the 6mA-StackingCV into a user-friendly webserver which is freely available at High quality dataset is very essential to construct a classifier for precisely identifying DNA 6mA sites. We used the same datasets as the i6mA-vote  which weOne-hot encoding is a simple but effective method to encode RNA/DNA/protein sequences. Each character in a sequence is encoded into a vector where only an element is 1 and other are zero. Here, A, C, G, and T are respectively are encoded into , , , and .The EIIP , 52 was We used a stacking ensemble model with cross validation for predicting 6mA sites whose idea originated from StackTADB , an effeTo quantitatively measure performance of methods, the following metrics: sensitivity (SN), Specificity (SP), accuracy (ACC), and Matthews’s correlation coefficient (MCC), were used, which were computed byAUC is defined as the area under the Receiver Operating Characteristic (ROC) curve which is drawn by linking true positive rates against false positive rates under various thresholds. The AUC ranges from 0 to 1, with larger values indicating better performance. An AUC of 1 represents perfect prediction, an AUC of 0.5 represents random guess, and an AUC of 0 represents completely reversed prediction.http://www.biolscience.cn/6mA-stackingCV/. As shown in Fig. We developed an online webserver to conveniently use the 6mA-StackingCV which is available at


The incidence of thyroid cancer (TC) continues to increase worldwide. Aerobic glycolysis, the prominent feature of glucose metabolism in cancer progression, is associated with TC. We first demonstrated that HIF-1a is highly expressed in TC tissues and is positively correlated with the level of XIST in the serum of patients with TC. Then, we proved that XIST regulates the expression of HIF-1a through the XIST/miR-93/HIF-1a pathway, thereby regulating the level of glycolysis in TC cells. Knockdown of XIST inhibits glycolysis, proliferation, the cell cycle and metastasis of TC cells. Finally, we verified that artemisinin could target the degradation of HIF-1a and inhibit glycolysis in TC cells. Collectively, XIST levels in serum may be used as a tumor marker for TC promoted by HIF-1a, which could be treated using artemisinin. The prevalence of thyroid cancer (TC), which is a malignant endocrine tumor, continues to increase worldwide . AccordiAerobic glycolysis, the prominent feature of glucose metabolism in cancer progression, is associated with the progression of multiple cancers and could support sustained malignant cancer cells . AerobicAll enrolled patients underwent surgical treatment at The Fourth Hospital of Hebei Medical University  from January 1, 2020, to December 31, 2020. Serum specimens were obtained from patients prior to surgery. The inclusion criteria were that the patient did not previously undergo radiation therapy and chemotherapy. A total of 40 patients who were admitted met the inclusion criteria. Overall, 14 patients were male, and 26 patients were female. The median age was 48 years of age (range 35–74 years).2 humidified incubator. TPC-1 cells were plated approximately 16 hours before transfection. Lentivirus for knockdown of XIST was ordered from Genehem , and the plasmid sequence of sh-XIST was as follows: 5’-TCTCTGTCATTGCTTCTGTAGTCACAGTC -3’. According to the results of preliminary experiments, the concentration of artemisinin in this study was 10 μmol/l. Cell viability was detected using MTS.The human TC cell line TPC-1 and normal thyroid cell line Nthy-ori 3–1 were purchased from Shanghai Zhongqiaoxinzhou Biotech, China. The cells were cultured in RPMI-1640 medium  with 10% heat-inactivated fetal bovine serum  at 37°C in a 5% CO2O2 and blocked with 5% normal goat serum in the proper sequence, followed by incubation with monoclonal anti-HIF-1a antibody  overnight at 4°C. The antibody was diluted in PBS buffer containing 5% normal goat serum. The negative control for each slide was incubated with 5% normal goat serum without the anti-HIF-1a antibody. The sections were then incubated with HRP-conjugated anti-rabbit IgG  for 45 min at 37°C and revealed with diaminobenzidine tetrahydrochloride. The stained slides were scored by three pathologists who were unaware of the clinical diagnosis. The indices of HIF-1a labeling were implemented so that samples were scored according to the percentage and intensity scores of positively stained tumor cells.Paraffin sections (4-mm thick) were deparaffinized and rehydrated, followed by treatment with 0.02 M EDTA buffer . Then, the sections were immersed in 3% HTotal RNA was extracted from tissue samples according to the instructions in the manual form iRNAVanaTM PARISTM . Briefly, 10 ng of total RNA was used as the template for 15 μl reverse transcription reactions. For each RNA, reactions were performed in triplicate using the 7500 RT‒PCR system (Applied Biosystems), and RNU66  was used as the normalization control. The sequence of the gene was as follows:Cells were lysed using precooled RIPA lysis buffer on ice for 45 minutes, and then the lysate was centrifuged at 12,000 rpm for 10 minutes at 4°C. The concentration of the extracted protein was determined using the BCA protein concentration determination kit, 50 μg of protein was loaded on the gel, separated by SDS‒PAGE gel, and transferred to the PVDF membrane. The membrane was blocked with 5% skimmed milk powder at room temperature for 1 hour and incubated with antibodies  at 4°C overnight. A concentration of 0.1% of Tween-containing TBST was washed 3 times and incubated with horseradish peroxidase-linked secondary antibody  for 1.5 hours at room temperature. The membrane was washed 3 times with TBST containing 0.1% Tween, and the immunoreactive bands were detected using a scanner ).The predicted 3’UTR sequences of XIST and HIF-1, which bind to miR-93-5p, were cloned from the genomic DNA of TC cells. Then, the sequence was inserted into the pmir-GLO control luciferase reporter vector. Luciferase reporter assays were conducted using the Dual-Glo luciferase assay system .A Seahorse Extracellular Flux (XF96e) Analyzer and the Agilent Seahorse XF Glycolytic Rate Assay Kit were used to measure the extracellular acidification rate (ECAR), reflecting the glycolytic level of live esophageal cancer cells. The specific experimental procedures were performed according to the manufacturer’s instructions.Cells were harvested and washed twice with FBS and then fixed in a 70% ethanol solution for 24 h at 4°C. Then, the fixed cells were washed once with PBS and resuspended as a single-cell suspension. The suspension was incubated with 500 μl of PI (50 μg/ml) for 30 min and tested by FCM according to standard procedures.We downloaded the protein molecule using the PDB database, and artemisinin was downloaded through the TCMSP platform. AutoDock4.2 was used for Molecular Docking. PyMOL and Ligplot were used for visualization. See our previous article for the specific operation procedure .All statistical analyses were performed using SPSS 21.0 software . Quantitative results are shown as the mean ± SD. The Student-Newman‒Keuls test was used for statistical analyses between the groups. P<0.05 was considered statistically significant.https://github.com/416057233/Artemisinin_thyroid-cancer_2023.git). The results showed that HIF-1a was highly expressed in cancer tissues (https://github.com/416057233/Artemisinin_thyroid-cancer_2023.git). The relative XIST expression level in serum was detected using U6 as a standard and was positively correlated with HIF-1a expression levels in cancer tissues (P<0.001) (HIF-1a RNA-seq gene expression data in cancer tissues and adjacent tissues of 570 TC patients were collected in the The Cancer Genome Atlas (TCGA) database  .Because the role of HIF-1a in cancer is to promote aerobic glycolysis, we tested the effects of XIST on the level of aerobic glycolysis in TC cells by ECAR. The results showed that knockdown of XIST significantly inhibited aerobic glycolysis levels . Then, wThe expression level of XIST was correlated with the expression level of HIF-1a, and we further verified the regulatory relationship between XIST and HIF-1a. We first detected the expression level of XIST in TPC-1 human TC cells and Nthy-ori 3–1 normal thyroid cells, and the results showed that XIST was significantly highly expressed in TPC-1 cells. We downregulated XIST  in the TThen, we validated the XIST/miR-93/HIF-1a pathway. We downregulated miR-93 in the cells with knockdown of XIST, and the western blot results showed that downregulation of miR-93 in the cells with knockdown of XIST could restore the expression of HIF-1a . AccordiA search of TCMSP and PharmMapper revealed that artemisinin was found to target HIF-1a, and we simulated the interaction between artemisinin and HIF-1a through molecular docking. The link between artemisinin and HIF-1a is shown in Cancer cells have faster growth and metabolism. Compared with normal cells, cancer cells have a unique mechanism of glycometabolism. As early as 1956, Warburg discovered that tumor tissue undergoes glycolysis even under conditions of sufficient oxygen, which is called the "Warburg" effect, or aerobic glycolysis . HIF-1a Biomarkers in serum have the advantages of convenient and noninvasive detection. XIST is stably expressed in serum and can be used as a tumor marker ,27. In tThe high expression of XIST in serum could be used as a tumor marker reflecting that the progression of TC may be caused by active aerobic glycolysis. We found that artemisinin could target HIF-1a directly and inhibit the aerobic glycolysis promoted by HIF-1a overexpression. Artemisinin, an extract from the plant Artemisia annua, is a well-known and efficacious antimalarial drug. It has also been proven to be able to inhibit cancer ,29. We fIn short, XIST serves as a ceRNA for miR-93, competing with HIF-1a for miR-93 binding, and finally promoting TC progression by regulating aerobic glycolysis. XIST levels in serum may be used as a tumor marker for TC promoted by HIF-1a, which could be treated by artemisinin.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 Raw images(PDF)Click here for additional data file.


Zeolite NaY synthesized from a typical procedure could suffer from agglomeration. Adding non-refluxed cogon grass (NG) to the synthesis gel could produce NaY with good dispersion and uniform crystal size. Small molecules produced from cogon grass in alkaline conditions could prevent agglomeration. The obtained zeolite (Y-NG) has a crystallinity and surface area comparable to the synthesis without grass (Y-WG). Y-NG demonstrated similar paraquat adsorption capacity to Y-WG at high initial concentrations. On the other hand, the zeolite from the addition of acid-refluxed grass (Y-RG) has the lowest crystallinity, smallest surface area, and poorest paraquat adsorption capacity. The effect of grass amount on the zeolite structure was studied. One gram of cogon grass was the optimum amount to add to the synthesis gel. Zeolite NaY is a member of the faujasite framework (FAU), with a typical silicon-to-aluminum ratio (Si/Al) in the range of 2–5 and sodium as a charge-balancing ion . NaY hasEfforts have been made in synthesis methods to optimize the characteristics of zeolites. One approach is the use of additives in one-pot synthesis, which allows for tailoring the properties of zeolites ,8. AdditImperata cylindrica) because it is abundant worldwide but has low value. Cogon grass is an invasive species widely regarded as undesirable due to its aggressive growth, high flammability, and ability to outcompete native plant species. It quickly spreads, forming dense stands and negatively impacting biodiversity [An interesting biomass in this work is cogon grass , hemicellulose (27.6%), lignin (16.5%), and extractives (11.2%) . When soTherefore, this work aims to utilize cogon grass to synthesize zeolite NaY. Non-refluxed and acid-refluxed grass are compared to understand the role of organic contents produced in situ within the synthesis gel. Characterization techniques, including X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) are employed to distinguish the properties of the obtained zeolites. Moreover, the adsorption of the herbicide paraquat is used as the first indicator to differentiate the zeolite samples. Previous works have reported that zeolite with different properties has different capacities for paraquat adsorption. The adsorption on zeolite NaY and NaBEA obeys the Langmuir model, with NaY as a better adsorbent . Further2SiO3) was prepared by dissolving 28.7 g of fumed silica in 59.8 mL of 0.27 M NaOH solution.Cogon grass was collected from the Suranaree University of Technology . The chemicals used were hydrochloric acid , silicon dioxide RPE , sodium aluminate , and sodium hydroxide . Commercial-grade paraquat  was used in the adsorption studies. Sodium silicate solution  water until pH neutral, and dried at 90 °C for 24 h. The refluxed grass is named RG.2O3:10SiO2:5.1Na2O:180H2O. Typically, 1.0 g of dried NG or RG was soaked with 10.0 mL of 2.5 M NaOH at ambient temperature for 24 h. These samples were named NGS and RGS. The mixture was transferred into a 250 mL polypropylene bottle and added to 4.8 g of Na2SiO3 solution and 0.4 g of NaAlO2 under continuous stirring. The resultant mixture, a seed gel, was aged at room temperature for 24 h.Zeolite NaY was synthesized with a process modified from that of the Synthesis Commission of the International Zeolite Association . The mol2 and 30.18 g of Na2SiO3 solution were added to NaOH solution containing 0.035 g of NaOH in 22.3 mL of DI water. After that, a feed gel was gradually dropped into the seed gel with stirring and continuously stirred for 10 min. The final gel was aged at ambient temperature and crystallized at 90 °C for 24 h. The resulting mixture was washed with DI water until the measured pH was neutral by centrifugation and dried at 90 °C for 24 h. The zeolite samples from non-refluxed and refluxed grass were designated as Y-NG and Y-RG, respectively. The same method was used to produce parent NaY zeolite without any grass. The sample was named Y-WG. The sample Y-NG was also called Y-1.0NG to compare the grass amounts  on absorbents was determined based on the Langmuir isotherm using Equation (1).eC and eq are the concentration and amount adsorbed at equilibrium, and LK is the Langmuir constant related to the affinity of the binding site (L/mg). Both LK and mq can be determined from the linear plot of eC/eq versus eC.The maximum adsorption capacity of paraquat  to proceed in multilayer sorption, while 1/n represents adsorption intensity.The Freundlich adsorption equation is defined based on Equation (2).2 adsorption/desorption using the Brunauer–Emmett–Teller (BET) method and the t-plot method. Thermal decomposition of the products was studied by thermogravimetric analysis  in the air-zero with a flow rate of 50.0 mL/min at a heating rate of 10.0 Phases of the samples were confirmed by X-ray diffraction  with Cu Kα radiation operated at 40 kV and 40 mA. Relative crystallinity was calculated from the XRD data. The total area of nine prominent peaks of FAU zeolite was determined by OriginLab, Origin 2022 (9.9). Y-NG has the highest total area and was used as a reference. The functional groups of the samples were investigated by Fourier-transform infrared spectroscopy  using attenuated total reflectance (ATR) mode. The morphology, particle size, and Si/Al ratio of the zeolite products were determined by field-emission scanning electron microscopy with energy-dispersive X-ray spectroscopy . The samples were dispersed on carbon tape and coated with gold for 1 min to increase their conductivity. Surface areas were determined by NZeolite NaY was synthesized with the procedure described in The synthesis of Y-NG was repeated twice with 1.0 g of non-refluxed grass from the same location. One gram of each zeolite product was calcined at 500 °C for 3 h with a heating rate of 1 °C/min. Before testing for paraquat adsorption, all non-calcined and calcined samples were characterized by XRD (Bruker D2 PHASER) and tungsten scanning electron microscopy .2) from fitting paraquat adsorption on the zeolite samples with the Langmuir  of Si-O-Si; 695 cm−1, symmetrical stretch (νsym) of O-T-O; and 454 cm−1, T-O bend. The peaks from the external linkages were assigned as follows: 571 cm−1 double-ring vibration; and 1130 cm−1 and 772 cm−1, asymmetrical and symmetrical stretches of O-T-O, respectively. It is worth noting that some peaks in the Y-RG spectrum exhibit lower intensity than others, such as the peak at 571 cm−1 compared to the peak at 695 cm−1. This observation aligns with the XRD results, indicating that Y-RG possesses the lowest crystallinity [allinity .−1 and the deformation band of adsorbed water at 1640 cm−1. Among the samples, Y-WG exhibited a stronger intensity in the first band than Y-NG and Y-RG, indicating that Y-WG possessed a higher degree of hydrophilicity.The peaks originating from hydroxyl groups were identified as follows : the asyThe SEM image of Y-NG d shows pThe SEM images of Y-RG in Regarding paraquat adsorption, both Y-NG and Y-RG seemed to have multilayer adsorption at the initial concentration of 1000 mg/L. It is possible that multilayer adsorption takes place on carbon compound residues or amorphous phases in Y-NG and Y-RG.To understand the composition of Y-NG, the sample was subjected to calcination at 500 °C for 3 h and investigated using SEM/EDX point analysis. The results are presented in 0 = 0.0–0.1), the adsorbed volume increases rapidly due to adsorption in the micropores, reaching a plateau as monolayer adsorption occurs. The surface area and pore volume of Y-NG are slightly smaller than those of Y-WG, potentially attributed to the presence of amorphous particles. Y-RG shows the lowest surface area and pore volume, primarily due to a significant amount of the amorphous phase. The findings from the nitrogen adsorption–desorption analysis align with the results obtained from the XRD and SEM observations.From the presented results, the addition of cogon grass to the synthesis of NaY could prevent the aggregation of zeolite crystals. Non-refluxed grass gave NaY a higher crystallinity and surface area than refluxed grass.Non-refluxed and refluxed cogon grass (NG and RG) were added to the synthesis of zeolite NaY to produce Y-NG and Y-RG. NaY was also synthesized without grass addition (Y-WG) for comparison. All samples adsorbed the paraquat according to the Langmuir model. The adsorption mainly takes place on zeolite. Y-NG has comparable crystallinity and surface area to Y-WG and better particle dispersion. In contrast, Y-RG displayed the lowest crystallinity, surface area, and paraquat adsorption capacity. This work demonstrates the potential of cogon grass to produce zeolite with uniform crystal size and good dispersion.Future research directions can focus on a deeper understanding of the role of compounds generated from cogon grass during zeolite synthesis and finding unique applications of the uniform-sized zeolite. This study exemplifies the potential of cogon grass as an abundant and natural resource for developing sustainable and environmentally friendly materials in adsorption and catalysis applications.